Current methods for fabricating lenses rely on mechanical processing of the lens or mold, such as grinding, machining and polishing. The complexity of these fabrication processes and the required specialized equipment prohibit rapid prototyping of optical components. This work presents a simple method that leverages the basic physics of surface tension and hydrostatics for rapidly fabricating a variety of lenses with optical surface quality and without the need for any mechanical processing. This approach allows to fabricate all types of spherical lenses(positive, negative, meniscus)over a wide range of sizes and curvatures, as well as lenses of special structure and topography(aspherical, saddle, bi-focal, doublet). The method is inexpensive, does not require specialized equipment, and can be implemented using a variety of curable liquids.

Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (2020)

Subjects

Computer Science - Human-Computer Interaction

Abstract

We present the Levitation Simulator, a system that enables researchers and designers to iteratively develop and prototype levitation interface ideas in Virtual Reality. This includes user tests and formal experiments. We derive a model of the movement of a levitating particle in such an interface. Based on this, we develop an interactive simulation of the levitation interface in VR, which exhibits the dynamical properties of the real interface. The results of a Fitts' Law pointing study show that the Levitation Simulator enables performance, comparable to the real prototype. We developed the first two interactive games, dedicated for levitation interfaces: LeviShooter and BeadBounce, in the Levitation Simulator, and then implemented them on the real interface. Our results indicate that participants experienced similar levels of user engagement when playing the games, in the two environments. We share our Levitation Simulator as Open Source, thereby democratizing levitation research, without the need for a levitation apparatus.Comment: 12 pages, 14 figures, CHI'20

Large-scale shape-changing interfaces have great potential, but creating such systems requires substantial time, cost, space, and efforts, which hinders the research community to explore interactions beyond the scale of human hands. We introduce modular inflatable actuators as building blocks for prototyping room-scale shape-changing interfaces. Each actuator can change its height from 15cm to 150cm, actuated and controlled by air pressure. Each unit is low-cost (8 USD), lightweight (10 kg), compact (15 cm), and robust, making it well-suited for prototyping room-scale shape transformations. Moreover, our modular and reconfigurable design allows researchers and designers to quickly construct different geometries and to explore various applications. This paper contributes to the design and implementation of highly extendable inflatable actuators, and demonstrates a range of scenarios that can leverage this modular building block.Comment: TEI 2020

Imaging systems are increasingly used as input to convolutional neural networks (CNN) for object detection; we would like to design cameras that are optimized for this purpose. It is impractical to build different cameras and then acquire and label the necessary data for every potential camera design; creating software simulations of the camera in context (soft prototyping) is the only realistic approach. We implemented soft-prototyping tools that can quantitatively simulate image radiance and camera designs to create realistic images that are input to a convolutional neural network for car detection. We used these methods to quantify the effect that critical hardware components (pixel size), sensor control (exposure algorithms) and image processing (gamma and demosaicing algorithms) have upon average precision of car detection. We quantify (a) the relationship between pixel size and the ability to detect cars at different distances, (b) the penalty for choosing a poor exposure duration, and (c) the ability of the CNN to perform car detection for a variety of post-acquisition processing algorithms. These results show that the optimal choices for car detection are not constrained by the same metrics used for image quality in consumer photography. It is better to evaluate camera designs for CNN applications using soft prototyping with task-specific metrics rather than consumer photography metrics.

Millimeter-Wave (mmWave) radar sensors are gaining popularity for their robust sensing and increasing imaging capabilities. However, current radar signal processing is hardware specific, which makes it impossible to build sensor agnostic solutions. OpenRadar serves as an interface to prototype, research, and benchmark solutions in a modular manner. This enables creating software processing stacks in a way that has not yet been extensively explored. In the wake of increased AI adoption, OpenRadar can accelerate the growth of the combined fields of radar and AI. The OpenRadar API was released on Oct 2, 2019 as an open-source package under the Apache 2.0 license. The codebase exists at https://github.com/presenseradar/openradar.

One of the key enablers of future wireless communications is constituted by massive multiple-input multiple-output (MIMO) systems, which can improve the spectral efficiency by orders of magnitude. However, in existing massive MIMO systems, conventional phased arrays are used for beamforming, which result in excessive power consumption and hardware cost. Recently, reconfigurable intelligent surface (RIS) has been considered as one of the revolutionary technologies to enable energy-efficient and smart wireless communications, which is a two-dimensional structure with a large number of passive elements. In this paper, we propose and develop a new type of high-gain yet low-cost RIS having 256 elements. The proposed RIS combines the functions of phase shift and radiation together on an electromagnetic surface, where positive intrinsic-negative (PIN) diodes are used to realize 2-bit phase shifting for beamforming. Based on this radical design, the world's first wireless communication prototype using RIS having 256 2-bit elements is designed and developed. Specifically, the prototype conceived consists of modular hardware and flexible software, including the hosts for parameter setting and data exchange, the universal software radio peripherals (USRPs) for baseband and radio frequency (RF) signal processing, as well as the RIS for signal transmission and reception. Our performance evaluation confirms the feasibility and efficiency of RISs in future wireless communications. More particularly, it is shown that a 21.7 dBi antenna gain can be obtained by the proposed RIS at 2.3 GHz, while at the millimeter wave (mmWave) frequency, i.e., 28.5 GHz, a 19.1 dBi antenna gain can be achieved. Furthermore, the over-the-air (OTA) test results show that the RIS-based wireless communication prototype developed is capable of significantly reducing the power consumption.Comment: The video demo is available in http://oa.ee.tsinghua.edu.cn/~dailinglong/publications/publications.html

To enable user diversity and multiplexing gains, a fully digital precoding multiple input multiple output (MIMO) architecture is typically applied. However, a large number of radio frequency (RF) chains make the system unrealistic to low-cost communications. Therefore, a practical three-stage hybrid analogue-digital precoding architecture, occupying fewer RF chains, is proposed aiming for a non-orthogonal IoT signal in low-cost multiuser MIMO systems. The non-orthogonal waveform can flexibly save spectral resources for massive devices connections or improve data rate without consuming extra spectral resources. The hybrid precoding is divided into three stages including analogue-domain, digital-domain and waveform-domain. A codebook based beam selection simplifies the analogue-domain beamforming via phase-only tuning. Digital-domain precoding can fine-tune the codebook shaped beam and resolve multiuser interference in terms of both signal amplitude and phase. In the end, the waveform-domain precoding manages the self-created inter carrier interference (ICI) of the non-orthogonal signal. This work designs over-the-air signal transmission experiments for fully digital and hybrid precoding systems on software defined radio (SDR) devices. Results reveal that waveform precoding accuracy can be enhanced by hybrid precoding. Compared to a transmitter with the same RF chain resources, hybrid precoding significantly outperforms fully digital precoding by up to 15.6 dB error vector magnitude (EVM) gain. A fully digital system with the same number of antennas clearly requires more RF chains and therefore is low power-, space- and cost- efficient. Therefore, the proposed three-stage hybrid precoding is a quite suitable solution to non-orthogonal IoT applications.

In the recent years of industrial revolution, 3D printing has shown to grow as an expanding field of new applications. The low cost solutions and short time to market makes it a favorable candidate to be utilized in the dynamic fields of engineering. Additive printing has the vast range of applications in many fields. This study presents the wide range of applications of the 3D printers along with the comparison of the additive printing with the traditional manufacturing methods have been shown. A tutorial is presented explaining the steps involved in the prototype printing using Rhinoceros 3D and Simplify 3D software including the detailed specifications of the end products that were printed using the Delta 3D printer.Comment: Accepted for oral presentation at 2019 Second International Conference on Latest trends in Electrical Engineering and Computing Technologies (Intellect 2019)

Logical frameworks are meta-formalisms in which the syntax and semantics of object logics and related formal systems can be defined. This allows object logics to inherit implementations from the framework including, e.g., parser, type checker, or module system. But if the desired object logic falls outside the comfort zone of the logical framework, these definitions may become cumbersome or infeasible. Therefore, the MMT system abstracts even further than previous frameworks: it assumes no type system or logic at all and allows its kernel algorithms to be customized by almost arbitrary sets of rules. In particular, this allows implementing standard logical frameworks like LF in MMT. But it does so without chaining users to one particular meta-formalism: users can flexibly adapt MMT whenever the object logic demands it. In this paper, we present a series of case studies that do just that, defining increasingly complex object logics in MMT. We use elegant declarative logic definitions wherever possible, but inject entirely new rules into the kernel when necessary. Our experience shows that the MMT approach allows deriving prototype implementations of very diverse formal systems very easily and quickly.Comment: In Proceedings LFMTP 2019, arXiv:1910.08712

Throughout the course of my Ph.D., I have been designing the user experience (UX) of various machine learning (ML) systems. In this workshop, I share two projects as case studies in which people engage with ML in much more complicated and nuanced ways than the technical HCML work might assume. The first case study describes how cardiology teams in three hospitals used a clinical decision-support system that helps them decide whether and when to implant an artificial heart to a heart failure patient. I demonstrate that physicians cannot draw on their decision-making experience by seeing only patient data on paper. They are also confused by some fundamental premises upon which ML operates. For example, physicians asked: Are ML predictions made based on clinicians' best efforts? Is it ethical to make decisions based on previous patients' collective outcomes? In the second case study, my collaborators and I designed an intelligent text editor, with the goal of improving authors' writing experience with NLP (Natural Language Processing) technologies. We prototyped a number of generative functionalities where the system provides phrase-or-sentence-level writing suggestions upon user request. When writing with the prototype, however, authors shared that they need to "see where the sentence is going two paragraphs later" in order to decide whether the suggestion aligns with their writing; Some even considered adopting machine suggestions as plagiarism, therefore "is simply wrong". By sharing these unexpected and intriguing responses from these real-world ML users, I hope to start a discussion about such previously-unknown complexities and nuances of -- as the workshop proposal states -- "putting ML at the service of people in a way that is accessible, useful, and trustworthy to all".Comment: This is an accepted position paper for the ACM CHI'19 Workshop

Access to vast amounts of data along with affordable computational power stimulated the reincarnation of neural networks. The progress could not be achieved without adequate software tools, lowering the entry bar for the next generations of researchers and developers. The paper introduces PyTorchPipe (PTP), a framework built on top of PyTorch. Answering the recent needs and trends in machine learning, PTP facilitates building and training of complex, multi-modal models combining language and vision (but is not limited to those two modalities). At its core, PTP employs a component-oriented approach and relies on the concept of a pipeline, defined as a directed acyclic graph of loosely coupled components. A user defines a pipeline using yaml-based (thus human-readable) configuration files, whereas PTP provides generic workers for their loading, training, and testing using all the computational power (CPUs and GPUs) that is available to the user. The paper covers the main concepts of PyTorchPipe, discusses its key features and briefly presents the currently implemented tasks, models and components.Comment: Paper accepted for SysML 2019 workshop at 33rd Conference on Neural Information Processing Systems (NeurIPS 2019)

Soft wearable robots are a promising new design paradigm for rehabilitation and active assistance applications. Their compliant nature makes them ideal for complex joints like the shoulder, but intuitive control of these robots require robust and compliant sensing mechanisms. In this work, we introduce the sensing framework for a multi-DoF shoulder exosuit capable of sensing the kinematics of the shoulder joint. The proposed tendon-based sensing system is inspired by the concept of muscle synergies, the body's sense of proprioception, and finds its basis in the organization of the muscles responsible for shoulder movements. A motion-capture-based evaluation of the developed sensing system showed conformance to the behaviour exhibited by the muscles that inspired its routing and validates the hypothesis of the tendon-routing to be extended to the actuation framework of the exosuit in the future. The mapping from multi-sensor space to joint space is a multivariate multiple regression problem and was derived using an Artificial Neural Network (ANN). The sensing framework was tested with a motion-tracking system and achieved performance with root mean square error (RMSE) of approximately 5.43 degrees and 3.65 degrees for the azimuth and elevation joint angles, respectively, measured over 29000 frames (4+ minutes) of motion-capture data.Comment: 8 pages, 7 figures, 1 table

As the complexity of state-of-the-art deep learning models increases by the month, implementation, interpretation, and traceability become ever-more-burdensome challenges for AI practitioners around the world. Several AI frameworks have risen in an effort to stem this tide, but the steady advance of the field has begun to test the bounds of their flexibility, expressiveness, and ease of use. To address these concerns, we introduce a radically flexible high-level open source deep learning framework for both research and industry. We introduce FastEstimator.

Haydi (http://haydi.readthedocs.io) is a framework for generating discrete structures. It provides a way to define a structure from basic building blocks and then enumerate all elements, all non-isomorphic elements, or generate random elements in the structure. Haydi is designed as a tool for rapid prototyping. It is implemented as a pure Python package and supports execution in distributed environments. The goal of this paper is to give the overall picture of Haydi together with a formal definition for the case of generating canonical forms.

Additive manufacture and rapid prototyping are versatile methods for the generation of lattice materials for applications in the creep regime. However, these techniques introduce defects that can degrade the macro-scopic creep strength. In the present study, the uniaxial tensile response of two-dimensional PMMA lattices is measured in the visco-plastic regime: tests are performed at 100C which is slightly below the glass transition temperature T g of PMMA. Both as-manufactured defects (Plateau borders and strut thickness variation) and as-designed defects (missing cell walls, solid inclusions, and randomly perturbed joints) are introduced. The dispersion in macroscopic strength is measured for relative densities in the range of 0.07 to 0.19. It is observed that initial failure of the lattice is diffuse in nature: struts fail at a number of uncorrelated locations, followed by the development of a single macroscopic crack transverse to the loading direction. In contrast, the same PMMA lattice fails in a correlated, brittle manner at room temperature. An FE study is performed to gain insight into the diffuse failure mode and the role played by as-manufactured defects, including the dispersion in tensile strength of individual struts of the lattice. A high damage tolerance to as-designed defects is observed experimentally: there is negligible knock-down in strength due to the removal of cell walls or to the presence of solid inclusions. These findings aid the design and manufacture of damage tolerant lattices in the creep regime.Comment: 30 pages, 11 figures. arXiv admin note: text overlap with arXiv:1904.10362

5G New Radio (NR) is an emerging radio access technology, which is planned to succeed 4G Long Term Evolution (LTE) as global standard of cellular communications in the upcoming years. This paper considers a digital signal processing model and a software implementation of a complete transceiver chain of the Physical Uplink Shared Channel (PUSCH) defined by the version 15 of the 3GPP standard, consisting of both baseband transmitter and receiver chains on a physical layer level. The BLER performance of the prototype system implementation under AWGN and Rayleigh fading channel conditions is evaluated. Moreover, the source code of high-level numerical model was made available online on a public repository by the authors. In the paper's tutorial part, the aspects of the 5G NR standard are reviewed and their impact on different functional building blocks of the system is discussed, including synchronization, channel estimation, equalization, soft-bit demodulation and LDPC encoding/decoding. A review of State-of-Art algorithms that can be utilized to increase the performance of the system is provided together with a guidelines for practical implementations.Comment: Conference: Signal Processing Symposium (SPSympo), Cracow, Poland, 17-19 Sep. 2019

Prototyping is one of the core activities of User-Centered Design (UCD) processes and an integral component of Human-Computer Interaction (HCI) research. For many years, prototyping was synonym of paper-based mockups and only more recently we can say that dedicated tools for supporting prototyping activities really reach the market. In this paper, we propose to analyze the evolution of prototyping tools for supporting the development process of interactive systems. For that, this paper presents a review of the literature. We analyze the tools proposed by academic community as a proof of concepts and/or support to research activities. Moreover, we also analyze prototyping tools that are available in the market. We report our observation in terms of features that appear over time and constitute milestones for understating the evolution of concerns related to the development and use of prototyping tools. This survey covers publications published since 1988 in some of the main HCI conferences and 118 commercial tools available on the web. The results enable a brief comparison of characteristics present in both academic and commercial tools, how they have evolved, and what are the gaps that can provide insights for future research and development.

Indoor positioning systems (IPS) are emerging technologies due to an increasing popularity and demand in location based service (LBS). Because traditional positioning systems such as GPS are limited to outdoor applications, many IPS have been proposed in literature. WLAN-based IPS are the most promising due to its proven accuracy and infrastructure deployment. Several WLAN-based IPS have been proposed in the past, from which the best results have been shown by so-called fingerprint-based systems. This paper proposes an indoor positioning system which extends traditional WLAN fingerprinting by using received signal strength (RSS) measurements along with channel estimates as an effort to improve classification accuracy for scenarios with a low number of Access Points (APs). The channel estimates aim to characterize complex indoor environments making it a unique signature for fingerprinting-based IPS and therefore improving pattern recognition in radio-maps. Since commercial WLAN cards offer limited measurement information, software-defined radio (SDR) as an emerging trend for fast prototyping and research integration is chosen as the best cost-effective option to extract channel estimates. Therefore, this paper first proposes an 802.11b WLAN SDR beacon receiver capable of measuring RSS and channel estimates. The SDR is designed using LabVIEW (LV) environment and leverages several inherent platform acceleration features that achieve real-time capturing. The receiver achieves a fast-rate measurement capture of 9 packets per second per AP. The classification of the propose IPS uses a support vector machine (SVM) for offline training and online navigation. Several tests are conducted in a cluttered indoor environment with a single AP in 802.11b legacy mode. Finally, navigation accuracy results are discussed.

Rapid prototyping is an emerging technology for the fast make of engineering components. A common technique is to laser cut a two-dimensional (2D) part from polymethyl methacrylate (PMMA) sheet. However, both manufacturing defects and design defects (such as stress raisers) exist in the part, and these degrade its strength. In the present study, a combination of experiment and finite element analysis is used to determine the sensitivity of the tensile strength of PMMA hexagonal lattices to both as-manufactured and as-designed defects. The as-manufactured defects include variations in strut thickness and in Plateau border radius. The knockdown in lattice tensile strength is measured for lattice relative density in the range of 0.07 to 0.19. A systematic finite element (FE) study is performed to assess the explicit role of each type of as-manufactured defect on the lattice strength. As-designed defects such as randomly perturbed joints, missing cells, and solid inclusions are introduced within a regular hexagonal lattice. The notion of a transition flaw size is used to quantify the sensitivity of lattice strength to defect size.Comment: 26 pages, 10 figures

In daily life, graphic symbols, such as traffic signs and brand logos, are ubiquitously utilized around us due to its intuitive expression beyond language boundary. We tackle an open-set graphic symbol recognition problem by one-shot classification with prototypical images as a single training example for each novel class. We take an approach to learn a generalizable embedding space for novel tasks. We propose a new approach called variational prototyping-encoder (VPE) that learns the image translation task from real-world input images to their corresponding prototypical images as a meta-task. As a result, VPE learns image similarity as well as prototypical concepts which differs from widely used metric learning based approaches. Our experiments with diverse datasets demonstrate that the proposed VPE performs favorably against competing metric learning based one-shot methods. Also, our qualitative analyses show that our meta-task induces an effective embedding space suitable for unseen data representation.Comment: Accepted to CVPR 2019